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Engineering Offer Breakdown

Hacker News

Engineering Offer Breakdown

It's always a bit confusing trying to understand what each companies offer actually means for your total comp each year. I made a simple tool that lets you input the relevant info and gives you a visual breakdown of your total comp over the years. Static site, no backend slurping up data Best part is the url updates in real time, so you can copy the url (or "Share this offer" button) to show someone else what an offer represents. - If you're a recruiter, this is an easy tool to help a candidate understand your offer - If you're asking a friend/mentor/spouse for advice, this is an easy way to give them context Hope it's helpful!

Visit productView on Hacker NewsSep 28, 2025
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Actual performance

2points
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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, visual, using · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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